Abstract. The root-mean-squared error (RMSE) and mean absolute (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it the reader decide which more relevant. In recent reprise 200-year debate Willmott Matsuura (2005) Chai Draxler (2014) give arguments favoring one metric or other. How...

Abstract. We consider the problem of estimating a vector x in the linear model Ax ≈ y, where A is a block circulant (BC) matrix with N blocks and x is assumed to have a weighted norm bound. In the case where both A and y are subjected to noise, we propose a minimax mean-squared error (MSE) approach in which we seek the linear estimator that minimizes the worst-case MSE over a BC structured unce...

Machine learning predictions algorithms have been used in different scenarios by earlier researchers. This article found the application of these for material deposition rate copper ions through high-speed selective jet electrodeposition (HSSJED). The presented emphasizes selection best suitable machine prediction among Gaussian process regression (GPR), support vector (SVM), and linear (LR) co...

A method for components instantaneous frequency (IF) estimation of multicomponent signals in low signal-to-noise ratio (SNR) is proposed. The method combines a new proposed modification of a blind source separation (BSS) algorithm for components separation, with the improved adaptive IF estimation procedure based on the modified sliding pairwise intersection of confidence intervals (ICI) rule. ...

readouts (R=4 for a 128x128 grid) and 256 samples per readout for the top row and 32 readouts and 512 samples (R=8 for a 256x256 grid) for the bottom row. A reference gradient echo scan is shown on the left. Mean squared error (units of 10) compared to the gradient echo scan is listed above the image. O-space with prephasing shows the lowest MSE, though strong asymmetric resolution is not obser...

Previously reported research efforts demonstrated that a genetic algorithm can evolve coefficients describing transforms that outperform standard wavelets, by reducing the mean squared error (MSE) apparent in reconstructed signals under conditions subject to quantization. This paper describes new results that substantially improve the state-of-the-art in evolved transform performance. Matched f...

This paper presents a comparison of statistical classical methods and machine learning algorithms for time series forecasting notably the Exponential Smoothing, hybrid ARIMA-GARCH model, K-Nearest Neighbors (KNN), Prophet, Long-Short Term Memory (LSTM). The data set used in this study is related to US inflation covers period from 1965 2021. performance models was evaluated using different metri...

Thermal conductivity and viscosity are crucial thermophysical properties of nanofluids. They play a pivotal role in industries involved with heat transfer applications. Alumina (Al2O3) nanoparticles known to be good additive for enhancement favorable results countless research. However, the measurement nanofluids through experimental is expensive. Therefore, random forest (RF), an advanced comp...

In this paper, a simplified model is used to qualitatively and quantitatively analyze two normalized data-reusing LMS algorithms when the input sequence is colored. The proposed approach relates the eigenvalue distribution of the input-signal autocorrelation matrix with probabilities of occurrence of subsequent parallel or orthogonal input-signal vectors. Closed-form formulas that describe stea...

This paper reviews the development of a root mean squared error (RMS) flaw sizing criterion. The effect of using an RMS error criterion for the sizing performance of the PISC II [1-3] inspection teams is investigated. A crucial part of establishing an RMS error sizing criterion is the decomposition of the components in the RMS error into the sum of the squares of the mean error, the standard er...